The present disclosure relates to a power management method and apparatus, a computing device, a medium, and a product. The power management method includes a monitoring step, a prediction step, an error calculation step and an adjustment step including adjusting power supply plan or a power demand of a user when at least one of a first error is greater than a first predetermined threshold or a second error is greater than a second predetermined threshold.
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4. The power management method according to claim 3, wherein the first time period is one month, and the second time period is one day.
5. The power management method according to claim 3, wherein the first prediction model is a first machine learning model obtained by training using power consumption historical data of the first time period as training data, and the second prediction model is a second machine learning model obtained by training using power consumption historical data of the second time period as training data.
6. The power management method according to claim 2, wherein the first time period is one month, and the second time period is one day.
7. The power management method according to claim 2, wherein the first prediction model is a first machine learning model obtained by training using power consumption historical data of the first time period as training data, and the second prediction model is a second machine learning model obtained by training using power consumption historical data of the second time period as training data.
8. The power management method according to claim 1, wherein the first time period is one month, and the second time period is one day.
9. The power management method according to claim 1, wherein the first prediction model is a first machine learning model obtained by training using power consumption historical data of the first time period as training data, and the second prediction model is a second machine learning model obtained by training using power consumption historical data of the second time period as training data.
11. A non-transitory machine readable storage medium storing an executable instruction that, when executed, causes a machine to perform the method according to claim 1.
15. The power management apparatus according to claim 13, wherein the first time period is one month, and the second time period is one day.
16. The power management apparatus according to claim 13, wherein the first prediction model is a first machine learning model obtained through training using power consumption historical data of the first time period as training data, and the second prediction model is a second machine learning model obtained through training using power consumption historical data of the second time period as training data.
17. The power management apparatus according to claim 12, wherein the first time period is one month, and the second time period is one day.
18. The power management apparatus according to claim 12, wherein the first prediction model is a first machine learning model obtained through training using power consumption historical data of the first time period as training data, and the second prediction model is a second machine learning model obtained through training using power consumption historical data of the second time period as training data.
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May 22, 2019
November 15, 2022
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